
pkg_ver = lambda pkg: "{:<20}{:}".format(pkg.__name__,pkg.__version__)

# ROOT
import uproot
print(pkg_ver(uproot))
import ROOT

# Machine Learning
import sklearn
print(pkg_ver(sklearn))
import torch
print(pkg_ver(torch))

# Data science
import scipy
print(pkg_ver(scipy))
import numpy
print(pkg_ver(numpy))
import pandas
print(pkg_ver(pandas))
import awkward
print(pkg_ver(awkward))

# Visualizations
import matplotlib
print(pkg_ver(matplotlib))
import matplotlib.pyplot as plt
import hvplot
print(pkg_ver(hvplot))
import hvplot.pandas
pandas.options.plotting.backend = 'holoviews'

import tqdm
print(pkg_ver(tqdm))
from tqdm.auto import tqdm
tqdm.pandas()

import glob
import re
import hashlib

uproot              5.0.7
Welcome to JupyROOT 6.28/02
sklearn             1.2.2
torch               2.0.0
scipy               1.10.1
numpy               1.23.5
pandas              1.5.3
awkward             2.2.0
matplotlib          3.7.1
hvplot              None

tqdm                4.62.3


%jsroot


class spectrum:
    
    def __init__(self, File_, Configuration_, Window_, Time_):  

        self.__FileName = File_
        self.__Configuration = Configuration_
        self.__Window = Window_
        self.__Time = Time_
        self.__File = uproot.open(File_)
        self.__DF = self.DF()
        self.__TH1D = self.TH1D()
        
    def __call__(self, ch):
        return numpy.polyval(self.__Calibration[::-1],ch)

    def File(self):
        return self.__File
    
    def FileName(self):
        return self.__FileName
    
    def DF(self):
        # Gets ak dataframe
        df_ak = self.__File['SCATTERER_A;1'].arrays(["TimeStamp", "Total_Deposited_Energy", "Pixel_multiplicity"],library="ak")
        #Transform from ak to pandas
        df = awkward.to_dataframe(df_ak)
        # Apply energy calibration to individual entries
        #df["Total_Deposited_Energy"] = df.Total_Deposited_Energy.progress_map(lambda x: numpy.polyval(
        #    calibrations_df.query("crystal == 'A0' & cw == '100'").iloc[0][[0,1,2]][::-1],x
        #))
        # Temperature df
        df_temp = self.__File['T_ASIC_0;1'].values()
        # Temperature change indexes
        temp_ch = numpy.concatenate((
            [df.TimeStamp.min()-1],
            numpy.mean(
                numpy.array(
                    [
                        df_temp[0][numpy.where(df_temp[1][:-1] != df_temp[1][1:])[0]],   # Gives the last value before change
                        df_temp[0][numpy.where(df_temp[1][:-1] != df_temp[1][1:])[0]+1], # Gives the first value after change
                    ]
                ),
                axis=0
            ),
            [df.TimeStamp.max()+1]
        ))
        # Add temperature information
        df["Temperature"] = df.TimeStamp.map(lambda x:
            numpy.concatenate((
                df_temp[1][numpy.where(df_temp[1][:-1] != df_temp[1][1:])[0]],
                [df_temp[1][-1]]
            ))[numpy.searchsorted(temp_ch, x)-1] 
        )
                        
        return df
    
    def TH1D(self):
        # Create histogram
        hist_dict = {i: ROOT.TH1F("",f"Events for {i}ºC;Energy [keV];Counts",2000,0,2000) for i in self.__DF.Temperature.unique()}
        # Add entries
        for i in self.__DF.iterrows():
            hist_dict[i[1].Temperature].Fill(i[1].Total_Deposited_Energy)
        # Scale
        for i in hist_dict:
            hist_dict[i].Scale(1/len(self.__DF.query("Temperature == @i").index))
        
        return hist_dict
    
    def get_TH1D(self):        
        return self.__TH1D
    
    def Configuration(self):
        return self.__Configuration
    
    def Window(self):
        return self.__Window


calp = glob.glob('/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/Calibration/**/*.CALp', recursive=True)

calibrations = []

for i in calp:
    line = pandas.read_csv(
        i,
        sep = "      :  ",
        skiprows=[0,4,5,6],
        header=None,
        engine="python"
    ).drop([0], axis=1).T
    
    line["crystal"] = i.split("/")[-1][:2]
    line["cw"] = i.split("/")[-1][5:8]
    
    calibrations.append(line)
    
calibrations_df = pandas.concat(calibrations)


def get_resolution(spectr,temp,en):
    
    TH1D = spectr.get_TH1D()[temp]
    
    #TH1D.GetXaxis().SetRange(TH1D.FindBin(en-250),TH1D.FindBin(en+600))
    TH1D.GetXaxis().SetRange(TH1D.FindBin(en-100),TH1D.FindBin(en+250))
    
    MaxBin   = TH1D.FindBin(TH1D.GetMaximumBin())
    
    ADC_Low  = MaxBin-100
    ADC_High = MaxBin+100

    gaussFit = ROOT.TF1("gaussFit", "gaus(0)+pol3(3)", ADC_Low, ADC_High)
    gaussFit.SetParameters(TH1D.GetMaximum(),MaxBin,7,0,0,0,0)
    TH1D.Fit(gaussFit,"QR")
    
    sigma = abs(gaussFit.GetParameter(2))
    centroid_ch = gaussFit.GetParameter(1)
    
    cal = lambda x: numpy.polyval(calibrations_df.query("crystal == 'A0' & cw == '100'").iloc[0][[0,1,2]][::-1],x)
    x1 = cal(centroid_ch+sigma*numpy.sqrt(2*numpy.log(2)))
    x2 = cal(centroid_ch-sigma*numpy.sqrt(2*numpy.log(2)))
    centroid = cal(centroid_ch)
        
    fwhm = x1-x2
            
    return sigma*numpy.sqrt(2*numpy.log(2))*2/centroid_ch*100, (fwhm/centroid)*100, centroid, (centroid_ch-en)/en*100, centroid_ch


def TH1D_draw(spectr,temp,en):
    
    canvas = ROOT.TCanvas()
    canvas.cd()
    
    TH1D = spectr.get_TH1D()[temp]
    
    #TH1D.SetTitle(repr(cell))
    TH1D.SetStats(False)
    
    latex = ROOT.TLatex()
    latex.SetNDC()
    latex.SetTextSize(0.03)
    
    TH1D.Draw("PE PLC")
    
    l1,l2,l3,l4,l5 = get_resolution(spectr,temp,en)
        
    latex.DrawText(0.7, 0.8, "R(ch): {:.2f}%".format(l1))
    latex.DrawText(0.7, 0.7, "R(en): {:.2f}%".format(l2))
    latex.DrawText(0.7, 0.6, "E: {:.0f}keV".format( l3))
    latex.DrawText(0.7, 0.5, "#Delta E: {:.2f}%".format(l4))
            
    return canvas


Data = {
    100: lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_19/Temperature_Cs137_center_fans_D.2023_05_19_T.15_50_00_C.itedABCD_lab_custom_2023.02.22_4.0v_887_45s_serie1_20_CW{win}.root",
    200: lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_26/Temperature_Cs137_center_D.2023_05_26_T.12_30_56_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW{win}.root",
    300: lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_26/Temperature_Cs137_center_D.2023_05_26_T.12_08_34_C.itedABCD_lab_custom_2023.02.22_4.0v_887_600s_CW{win}.root",
    400: lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_29/Temperature_Na22_D.2023_05_29_T.11_57_12_C.itedABCD_lab_custom_2023.02.22_4.0v_887_900s_CW{win}.root",
    500: lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_29/Temperature_Cs137_D.2023_05_29_T.12_35_13_C.itedABCD_lab_custom_2023.02.22_4.0v_887_900s_CW{win}.root",
    1:  lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_53_56_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie1_40_CW{win}.root",
    2:  lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_54_14_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie2_40_CW{win}.root",
    3:  lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_54_33_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie3_40_CW{win}.root",
    4:  lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_54_51_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie4_40_CW{win}.root",
    5:  lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_55_10_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie5_40_CW{win}.root",
    6:  lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_55_29_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie6_40_CW{win}.root",
    7:  lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_55_47_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie7_40_CW{win}.root",
    8:  lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_56_06_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie8_40_CW{win}.root",
    9:  lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_56_24_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie9_40_CW{win}.root",
    10: lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_56_43_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie10_40_CW{win}.root",
    11: lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_57_02_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie11_40_CW{win}.root",
    12: lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_57_20_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie12_40_CW{win}.root",
    13: lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_57_39_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie13_40_CW{win}.root",
    14: lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_57_57_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie14_40_CW{win}.root",
    15: lambda win: f"/run/media/bgameiro/d043b5e4-57a4-457e-8839-cb1adc9c72bc/Data/data_2023_05_22/Temperature_Cs137_center_D.2023_05_22_T.15_58_16_C.itedABCD_lab_custom_2023.02.22_4.0v_887_15s_serie15_40_CW{win}.root",
}


en=511
cal = calibrations_df.query("crystal == 'A0' & cw == '100'").iloc[0][[0,1,2]][::-1]
p = numpy.poly1d(cal)
en = list(set(i for i in (p - en).roots if i > 0))[0]
en

206.419806623866


entries = []

for i in tqdm(Data):
    for CW in tqdm([100,200]):
        
        file = Data[i](CW)
        
        spectr = spectrum(
                file,
                1,
                CW,
                int(re.search("_(\d{2,4})s_", file).group(1))
        )
        
        open_file = uproot.open(file)
        
        en = 661.7 if "Cs137" in file else 511
        
        cal = calibrations_df.query("crystal == 'A0' & cw == '100'").iloc[0][[0,1,2]][::-1]
        p = numpy.poly1d(cal)
        en = list(set(i for i in (p - en).roots if i > 0))[0]
        
        for temp in tqdm(pandas.Series(open_file['T_ASIC_0;1'].values()[1]).unique()):


            res_ch, res_en, cent, fit, cent_ch = get_resolution(spectr,temp,en)

            entries.append(
                pandas.DataFrame({
                    "resolution": res_en,
                    "fit": fit,
                    "cw": CW,
                    "obj": spectr,
                    "peak_en": cent,
                    "peak_ch": cent_ch,
                    "temperature": temp,
                    "measurement": hashlib.sha1(file[:-10].encode("UTF-8")).hexdigest()[:5],
                    "energy": en,
                }, index=[0])
            )
                    
entries_df = pandas.concat(entries, ignore_index=True)

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entries_df


entries_df.to_pickle('entries_df_all.pickle.gzip',compression='gzip')


entries_df.hvplot.scatter("temperature","peak_ch",by=["energy","cw"],hover_cols=["measurement"])


entries_df.hvplot.scatter("temperature","peak_en",by=["energy","cw"],hover_cols=["measurement"])


entries_df.hvplot.scatter("temperature","deviation_ch",by=["energy","cw"],hover_cols=["measurement"])


entries_df.hvplot.scatter("temperature","fit",by=["energy","cw"],hover_cols=["measurement"])


entries_df.hvplot.scatter("temperature","resolution",by=["energy","cw"],hover_cols=["measurement"])


entries_df.query("temperature == 30.4")


#TH1D_draw(entries_df.iloc[211].obj,30.4,275).Draw()

	resolution	fit	cw	obj	peak_en	peak_ch	temperature	measurement	deviation_ch	energy
0	6.104301	29.415677	100	<__main__.spectrum object at 0x7f4ba4888820>	856.343535	363.885632	24.6	6e1cf	31.794869	661.7
1	6.179620	28.173558	100	<__main__.spectrum object at 0x7f4ba4888820>	848.124430	360.226862	24.8	6e1cf	30.469707	661.7
2	6.152740	27.843299	100	<__main__.spectrum object at 0x7f4ba4888820>	845.939111	359.253362	25.1	6e1cf	30.117118	661.7
3	6.160810	26.310295	100	<__main__.spectrum object at 0x7f4ba4888820>	835.795222	354.730702	25.2	6e1cf	28.479066	661.7
4	6.307583	25.595279	100	<__main__.spectrum object at 0x7f4ba4888820>	831.063964	352.619104	25.5	6e1cf	27.714272	661.7
...	...	...	...	...	...	...	...	...	...	...
241	6.140938	-4.710555	200	<__main__.spectrum object at 0x7f49510bd120>	630.657791	261.873507	31.4	a7382	-5.152660	661.7
242	6.532014	-5.024121	100	<__main__.spectrum object at 0x7f4ba41f3d00>	628.593332	260.925062	31.3	030fd	-5.496175	661.7
243	6.506611	-5.217419	100	<__main__.spectrum object at 0x7f4ba41f3d00>	627.220079	260.294008	31.4	030fd	-5.724734	661.7
244	6.281325	-4.620199	200	<__main__.spectrum object at 0x7f4ba4279ed0>	631.207892	262.126183	31.3	030fd	-5.061143	661.7
245	6.291890	-4.956720	200	<__main__.spectrum object at 0x7f4ba4279ed0>	628.953232	261.090426	31.4	030fd	-5.436282	661.7

	resolution	fit	cw	obj	peak_en	peak_ch	temperature	measurement	test	energy
211	6.413937	140.536152	100	<__main__.spectrum object at 0x7f4ba4589ba0>	664.121357	277.207468	30.4	a15b3	13.987299	661.7
213	6.073674	141.229504	200	<__main__.spectrum object at 0x7f4b4fdb9ba0>	666.035704	278.082425	30.4	a15b3	12.220411	661.7
214	6.495296	139.062356	100	<__main__.spectrum object at 0x7f4ba478f730>	660.052199	275.346843	30.4	b038e	27.037095	661.7
216	6.315008	139.652253	200	<__main__.spectrum object at 0x7f4b78482b00>	661.680908	276.091703	30.4	b038e	25.857501	661.7